Optic Disc Segmentation

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa
Sep 22, 2026Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye +4Fundus Imaging Medical Diagnosis
Department of Ophthalmology, Emory University, Atlanta, Georgia, United States · Department of Biomedical Engineering, National University of Singapore, Singapore · Singapore Eye Research Institute, Singapore National Eye Centre, Singapore +5
Bayesian-Optimized Superpixel-GrabCut for Traceable Optic Disc Segmentation
Aug 29, 2026Shraddha Changune, Vivek Noel Soren, Gautam Das +1Optic Disc Segmentation Medical Image Segmentation
Electrical Engineering Indian Institute of Technology Delhi, India · Electronics and Communication Engineering Gauhati University Guwahati, India
OptiModNet: A UNet-Transformer Hybrid with Grouped-Query and Channel Attention for Optic Disc and Cup Segmentation
Aug 19, 2026Soumili Ghosh, Debapriya Roy, Aryan Das +1Optic Disc Segmentation Ophthalmic Image Analysis
IEM Saltlake,India · Techno Main Salt Lake, India · VIT Bhopal University, India +1
Optic Disc Segmentation in Fundus Images: From Classical Image Processing and Deformable Models to Modern AI
Aug 18, 2026Buket D. BarkanaOptic Disc Segmentation Ophthalmic Image Analysis
Biomedical Engineering Department, The University of Akron, Akron, OH 44325, USA
UPolarSQ: Polar Representation Learning for Optic Disc and Peripapillary Atrophy Segmentation and Quantification in Fundus Photographs
Aug 9, 2026Mengxian He, Yunyun sun, Ziyue Gao +3Medical Image Analysis Medical Image Segmentation
Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · Capital Medical University, Beijing, China
Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening
Apr 10, 2026Rimsa Goperma, Rojan Basnet, Liang ZhaoOptic Disc Segmentation Glaucoma Detection
Graduate School of Advanced Integrated Studies in Human Survivability (GSAIS), Kyoto University, Kyoto 606-8306, Japan
Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation
Mar 30, 2026Shramana Dey, Varun Ajith, Abhirup Banerjee +1Optic Disc Segmentation Domain Generalization
Indian Statistical Institute, Kolkata, West Bengal, India · Department of Engineering Science, University of Oxford, Oxford, United Kingdom · Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis
Mar 15, 2026Zhiwei Wang, Yuxing Li, Meilu Zhu +2Optic Disc Segmentation Glaucoma Detection
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China · College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

Sep 22, 2026J/K move · Enter open · S save

Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa

Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye, Martin Buist+3

Department of Ophthalmology, Emory University, Atlanta, Georgia, United States · Department of Biomedical Engineering, National University of Singapore, Singapore · Singapore Eye Research Institute, Singapore National Eye Centre, Singapore+5

Fundus Imaging Medical Diagnosis Medical Image Segmentation Glaucoma Detection Explainable Medical Image Analysis Optic Disc Segmentation+1

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Purpose: To develop and evaluate an interpretable artificial intelligence (AI) framework for glaucoma screening from low-cost portable, handheld retinal fundus photographs in a West African population and to compare its performance with clinical tabletop fundus imaging. Methods: We used data from a community-based study of 681 participants (1,362 eyes) in Nigeria, comprising 414 glaucoma, 478 glaucoma suspect, and 470 non-glaucoma eyes. Fundus photographs were acquired using the low-cost handheld, portable Volk Viva retinal camera and the Canon CR-2-AF tabletop camera. We fine-tuned component models separately to each device to perform vessel segmentation, cup and disc boundary segmentation, and feature extraction to detect optic nerve head features. A final classification model combined these components to classify scans as glaucoma, glaucoma suspect or non-glaucoma. Feature-weight analysis and Gradient-weighted Class Activation Mapping were used for interpretation. Results: The models performed well on both Volk Viva and Canon CR-2-AF images: Vessel segmentation: 0.98 Dice Coefficient (DC) (Volk) and 0.94 DC (Canon); Cup and disc segmentation: 0.95 DC (Volk) and 0.96 DC (Canon); Optic nerve head feature detection: area under the receiver operating characteristic curve (AUCs) of 0.83

\pm

0.03 (Volk) and 0.87

\pm

0.04 (Canon); Classification model: AUCs of 0.85

\pm

0.01 (Volk) and 0.93

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0.01 (Canon). Reports for each image, present model decision confidence scores and decision-rationale visualizations to support clinical interpretation. Conclusions: Volk Viva results were reasonably comparable to Canon CR-2-AF in the component models and not far behind in classification. This shows that interpretable AI combined with low-cost, portable imaging may enhance community-level glaucoma screening, especially in settings with limited specialist access and resources.